In the field of pattern recognition, classifiers are used to classify an object into one of a number of predefined classes or categories. Applications for classifiers include speech recognition, face recognition, image processing, and medical diagnosis.
Classifiers are used in image processing to classify pixels or regions in an image into one of a number of predefined classes. For example, a classifier may be used to classify regions in an image of natural scenery into one of a number of classes such as leaves, grass, or sky. In the medical field, classifiers are used to classify regions in images of patients into different types of tissue, for example, abnormal or diseased tissue and normal tissue.
A classifier can be customized for a particular classification problem by training the classifier to identify particular classes. This usually involves a “training” process, in which a generic classifier is presented with a large number of example image regions of each category or class. The classifier extracts features (also known as patterns) associated with each image region and “learns” to associate these features (patterns) with the known category or class of the region. The learning processing can be, for example, one in which parameter values in a mathematical model are adjusted until the classifier ideally produces the correct class for each training input. Once the classifier has been trained to identify the classes, the classifier can be used to classify new input image regions by applying the previously learned associations.
In most practical applications, for a given input image region, the output of the classifier is at best correct only in a statistical sense. Errors are inevitable in real world classification problems. The overall accuracy of a classifier can be stated by computing a percentage of training inputs that are correctly classified.
In the medical field, classification is used to classify tissues in medical images, e.g., an intravascular ultrasound (IVUS) image. FIG. 1a shows an example of an imaging transducer assembly 1. The imaging transducer 1 is typically within the lumen 10 of a guidewire (partially shown), having an outer tubular wall member 5. To obtain an image of a blood vessel, the imaging transducer assembly 1 may be inserted into the vessel. The transducer assembly 1 may then rotate while simultaneously emitting energy pulses, e.g., ultrasound waves, at portions of the vessel from within the vessel and receiving echo or reflected signals.
Turning to FIG. 1b, it is known in the art that an imaging console 20 having a display screen, a processor and associated graphics hardware (not shown) may be coupled with the imaging transducer assembly 1 to form a medical imaging system 30. The imaging console 20 processes the received echo signals from the imaging transducer assembly 1 and forms images of the area being imaged. To form the images, the imaging console 20 draws multiple lines, known as “radial lines” (not shown) on the display screen that each correspond to an angular position of the transducer assembly 1. The processor of the imaging console 20 assigns brightness values to pixels of the lines based on magnitude levels of the echo signals received from the transducer assembly 1 at the angular positions corresponding to the lines. A drawing that includes a large number of these radial lines results in an image such as an intravascular ultrasound (IVUS) image (not shown). Such an image may show, among other things, the texture of the area being imaged, such as the smoothness or the roughness of the surface of the area being imaged.